A Framework for 'Smart' Computational Micromechanics

A Framework for 'Smart' Computational Micromechanics

Kiefer, Joshua (TU Delft Aerospace Engineering)

Turteltaub, S.R. (mentor)
Lopes, Claudio (mentor)

Delft University of Technology

Aerospace Engineering

2018-10-30

More than half a century after the first application of composite materials in aircraft, the accurate prediction of their failure remains a pressing and unresolved issue. Another important limitation continues to be the low transverse strength of unidirectional composite plies, which can lead to premature failure in common laminates such as the cross-ply. A tool that promises improvements on both of these fronts is computational modeling. With ever increasing computing power, today’s multi-scale-models are able to simulate the various failure modes across the relevant length scales, and allow a new level of optimization by accurately determining the contributing material parameters.

To fully exploit this capability, we propose a ‘smart’ framework, combining Computational Modeling, Design of Experiments, and Neural Networks. It is developed with the aim to create analytical surrogate models of complex material properties, in a fully automated way, and based on a minimum amount of computer simulations. While such a framework can be universally applied, we will showcase our results for one possible application: the prediction and optimization of the transverse strength of a unidirectional composite ply.

Generating Statistical Volume Elements of the material at microscale, we use a Computational Micromechanics model to compute the ply’s strength under transverse loading. By Design of Experiment principles, we then explore the interdependent influences of the main geometrical and material parameters. Finally, we obtain an analytical surrogate model of the transverse strength by employing a Neural Network.

Putting to use the developed ‘smart’ framework, the effects of the following parameters were studied: constituent strengths (matrix, fiber-matrix-interface), fiber volume fraction, shape of fiber cross-section (circular or non-circular), and fiber diameter. The results confirm and quantify the primary influence of the constituent strengths, followed by the secondary effect of the fiber volume fraction. Non-circular fiber cross-sections were found to increase transverse compressive strength, and mixing of different fiber diameters may increase slightly both tensile and compressive strength.

In addition to presenting the resulting surrogate models, challenges in the development and automation of the framework will be discussed, and the case will be made for wider application of such a framework.

Design of Experiments
Computational Micromechanics
Neural Networks
Composites
Transverse Strength
Fiber Shape
Hybrid

http://resolver.tudelft.nl/uuid:c851e9a1-9ade-4fee-98f6-1cd830a83898

Student theses

master thesis

© 2018 Joshua Kiefer